Navigating 20 AI Use Cases in Healthcare: From Hype to Evidence!
Bertalan Meskó, MD, PhD
The Medical Futurist, Author of Your Map to the Future, Global Keynote Speaker, and Futurist Researcher
It's incredibly hard to see through the current AI hype in healthcare. Myriads of potential uses cases, a lot of companies with commercial interest and changing regulations dominate the field.
To see what is really going on and what we can realistically expect from AI to deliver in practice, we have mapped the rapidly expanding universe of AI use cases in healthcare from early-stage “on the horizon” innovations to “safe bets” that are already backed by strong evidence.
We analyzed them on two scales, little evidence to evidence-based (meaning there are studies and peer-reviewed papers proving their efficiency and safety); and low risk to high risk (meaning patients' lives might be at stake in case of an error).
This yielded four groups:
1) Speculative and risky (little evidence, high risk)
2) On the horizon (little evidence, low risk)
3) Handle with care (evidence-based, high risk)
4) Safe bet (evidence-based, low risk)
Use cases and their categories
Speculative & Risky
Autonomous AI prescribing
Mental health chatbots
Treatment plans and drug interactions
Simulate trial designs
Digital twins for personalized simulations
On The Horizon
Predictive analytics for patient outcomes
Patient education
Disease screening and diagnostics
Track medication adherence
Resource allocation
Handle With Care
AI-driven biomarker and drug discovery
AI-powered clinical documentation
AI-assisted robotic surgery
Radiology analysis
Automated insurance claims processing
Safe Bet
Monitor wearables and vital signs
Triaging patients in ER
ECG interpretation
Surgical planning
Patient scheduling
We hope this infographic helps clarify the path ahead: which solutions demand more research and caution (e.g. autonomous AI prescribing, mental health chatbots), and which are ready for prime time (e.g. AI-powered clinical documentation, radiology analysis, ECG interpretation).
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4moThanks, Bertalan. What do you think about Mental Health chatbots, which are in a high-risk zone but quite popular among users?
Professor at Former Professor of Microbiology TMC Kollam Kerala
4moWell written and explained article whether one like it or not to implement and many end users like Doctors in practice and all the time caring Nurses try using the matter to their advantage Dr.T.V. Rao MD
Medical Expert, Board Advisor, Angel Investor
4moThis framework is excellent, especially the axis of evidence vs. risk. One area I think deserves more clarity is the line between “triage” and “clinical decision support.” Some systems are still using intermittent data and rule-based logic, while others are starting to layer in predictive models and cross-device signal aggregation. As someone building in this space, I’m seeing firsthand how messy the deployment vs. evidence conversation gets in real clinical settings. Curious: how would you categorize AI tools that augment bedside decisions without replacing them?
Physician | Clinical Development-Driven Pharmaceutical & Product Strategy + BD | Helping Pharma Leaders De-Risk & Advance High-Potential $500M+ Assets to Fuel Portfolios | Executive MBA ’26 | English–Japanese-Hindi/Urdu
4moBertalan Meskó, MD, PhD I'm very surprised "Triaging patients in ER" is on the safe bet side. Furthermore, mental health chatbots appear to offer potential, especially if their focus is on companionship – this may be particularly relevant socially in Japan and for the elderly.
Marketing Manager | Brand Management | Branding | Strategic Planning | Innovation | Team Management | Creative Design | Advertising and Propaganda | Data Analysis | Power BI
4moHad to repost this, such a great post. Thanks for the clear and insightful chart! 😀